Characterizing the role of Ca <sup>2+</sup> fluxes in defining fast and slow components of spontaneous Ca <sup>2+</sup> local transients in OPCs using computational modeling
Bibliographic record
Abstract
1 Abstract Spontaneous Ca 2+ local transients (SCaLTs) in isolated oligodendrocyte precursor cells (OPCs) are largely regulated by the following fluxes: store-operated Ca 2+ entry (SOCE), Na + /Ca 2+ exchange (NCX), Ca 2+ pumping through Ca 2+ -ATPases, and Ca 2+ -induced Ca 2+ -release through Ryanodine receptors (RyR) and inositoltriphosphate receptors (IP 3 R). However, the relative contributions of these fluxes in mediating fast spiking and slow baseline oscillations seen in SCaLTs remain incompletely understood. Here, we developed a stochastic spatiotemporal computational model to simulate SCaLTs in a homogeneous medium with ion flow between the extracellular, cytoplasmic and endoplasmic-reticulum compartments. By simulating the model and plotting both the histograms of SCaLTs obtained experimentally and from the model as well as the standard deviation of interspike intervals (ISI) against ISI averages of multiple model and experimental realizations we revealed that: SCaLTs exhibit very similar characteristics between the two datasets, they are mostly random, they encode information in their frequency, and the slow baseline oscillations could be due to the stochastic slow clustering of IP 3 R (modeled as an Ornstein-Uhlenbeck noise process). Bifurcation analysis of a deterministic temporal version of the model shows that the contribution of fluxes to SCaLTs depends on the parameter regime and that the combination of excitability, stochasticity, and mixed-mode oscillations are responsible for irregular spiking and doublets in SCaLTs. Additionally, our results demonstrate that blocking each flux reduces SCaLTs frequency and that the reverse (forward) mode of NCX decreases (increases) SCaLTs. Taken together, these results provide a quantitative framework for SCaLT formation in OPCs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".